What is one of the advantages of using clusters in Databricks?

Using clusters in Databricks enables distributed computing, which is especially beneficial for processing large-scale data. This strategic advantage allows for the parallel execution of tasks across multiple nodes in a cluster, significantly enhancing computational power and efficiency. When working with extensive datasets or complex computations, the ability to distribute workloads means that operations can be performed much faster compared to a single-node environment.

The scalability offered by clusters allows teams to handle data volumes that wouldn't be feasible on traditional infrastructures. It supports resource optimization, where tasks are efficiently allocated based on availability and workload, ensuring that jobs finish swiftly and resources are not wasted. This distributed approach is integral for advanced analytics, machine learning algorithms, and any computational tasks requiring significant processing capabilities.

In contrast, the other options focus on limitations or features not primarily associated with clusters. For instance, limiting computational resources or manual data entry falls outside the primary purpose of clusters, while the automatic saving of user activities pertains more to session management and data persistence rather than the operational strengths of clusters.